A Comparison of Circumgalactic Mg ii Absorption between the TNG50 Simulation and the MEGAFLOW Survey

The circumgalactic medium (CGM) contains information on gas flows around galaxies, such as accretion and supernova-driven winds, which are difficult to constrain from observations alone. Here, we use the high-resolution TNG50 cosmological magnetohydrodynamical simulation to study the properties and kinematics of the CGM around star-forming galaxies in 1011.5–1012 M ⊙ halos at z ≃ 1 using mock Mg ii absorption lines, which we generate by postprocessing halos to account for photoionization in the presence of a UV background. We find that the Mg ii gas is a very good tracer of the cold CGM, which is accreting inward at inflow velocities of up to 50 km s−1. For sight lines aligned with the galaxy’s major axis, we find that Mg ii absorption lines are kinematically shifted due to the cold CGM’s significant corotation at speeds up to 50% of the virial velocity for impact parameters up to 60 kpc. We compare mock Mg ii spectra to observations from the MusE GAs FLow and Wind (MEGAFLOW) survey of strong Mg ii absorbers (EW2796 Å 0 > 0.5 Å). After matching the equivalent-width (EW) selection, we find that the mock Mg ii spectra reflect the diversity of observed kinematics and EWs from MEGAFLOW, even though the sight lines probe a very small fraction of the CGM. Mg ii absorption in higher-mass halos is stronger and broader than in lower-mass halos but has qualitatively similar kinematics. The median-specific angular momentum of the Mg ii CGM gas in TNG50 is very similar to that of the entire CGM and only differs from non-CGM components of the halo by normalization factors of ≲1 dex.

Discriminating Chiral Insertions from BSM for the Muon (g-2) by non-Abelian Zero Modes

A sorting out method, between the New Physics and as vs. the Lepton Flavor Violated Chiral insertion is exposed, interpreting so the Light-by-Light scattered amplitude deviation in the muon magnetic moment, with a novel 2-color instead of 3-color in diagrams projective polarizations. The calculation is done using an extension-detention of the muon resonance interaction internal lines (which may be generically of a non-QCD nature)such its modes’ phonons decompose into instantons while the helicities are to meet the imposed polarizations. A confirmation comes out from a non-applicability of the Landau Gauge, to the one of the cases, the tri-vector-mode, giving it a double pole in its Goldstone propagator, vs. its truth in the Sudakov type with a single pole mode Goldstone propagator.The fits between expansions of the phonon derived from slicing’s, and the instanton derived from inverse arguments of differently coupled cosine’s, are surprisingly proportional to their projected (2 to 3 in their extended diagrams) normalization factors paving ways into a BSM popped up selectivity method.

Systematic analysis of candidate reference genes for gene expression analysis in hyperoxia-based mouse models of bronchopulmonary dysplasia

Bronchopulmonary dysplasia is a chronic lung disease of preterm infants. Mouse models of hyperoxia-induced lung injury are often used to study pathogenesis and potential therapeutic approaches of BPD. Beside histological studies, gene expression analysis of lung tissue is typically used as experimental read out. RT-qPCR is the standard method for gene expression analysis, however, the accuracy of the quantitative data depends on the appropriate selection of reference genes. No data on validated reference genes for hyperoxia-induced neonatal lung injury in mice is available. In this study, 12 potential reference genes were systematically analyzed for their expression stability in lung tissue of neonatal mice exposed to room air or hyperoxia and healthy adult controls using published software algorithms. Analysis of gene expression data identified Hprt, Tbp and Hmbs as the most stable reference genes and proposed combinations of Hprt/Sdha or Hprt/Rpl13a as potential normalization factors. These reference genes and normalization factors were validated by comparing Il6 gene and protein expression and may facilitate accurate gene expression analysis in lung tissues of similar designed studies.

Rank normalization empowers a t-test for microbiome differential abundance analysis while controlling for false discoveries

A major task in the analysis of microbiome data is to identify microbes associated with differing biological conditions. Before conducting analysis, raw data must first be adjusted so that counts from different samples are comparable. A typical approach is to estimate normalization factors by which all counts in a sample are multiplied or divided. However, the inherent variation associated with estimation of normalization factors are often not accounted for in subsequent analysis, leading to a loss of precision. Rank normalization is a nonparametric alternative to the estimation of normalization factors in which each count for a microbial feature is replaced by its intrasample rank. Although rank normalization has been successfully applied to microarray analysis in the past, it has yet to be explored for microbiome data, which is characterized by high frequencies of 0s, strongly correlated features and compositionality. We propose to use rank normalization as an alternative to the estimation of normalization factors and examine its performance when paired with a two-sample t-test. On a rigorous 3rd-party benchmarking simulation, it is shown to offer strong control over the false discovery rate, and at sample sizes greater than 50 per treatment group, to offer an improvement in performance over commonly used normalization factors paired with t-tests, Wilcoxon rank-sum tests and methodologies implemented by R packages. On two real datasets, it yielded valid and reproducible results that were strongly in agreement with the original findings and the existing literature, further demonstrating its robustness and future potential. Availability: The data underlying this article are available online along with R code and supplementary materials at https://github.com/matthewlouisdavisBioStat/Rank-Normalization-Empowers-a-T-Test.

Star-triangle type relations from 2d $$ \mathcal{N} $$ = (0, 2) USp(2N) dualities

Inspired by the gauge/YBE correspondence this paper derives some star-triangle type relations from dualities in 2d$$ \mathcal{N} $$ N = (0, 2) USp(2N) supersymmetric quiver gauge theories. To be precise, we study two cases. The first case is the Intriligator-Pouliot duality in 2d$$ \mathcal{N} $$ N = (0, 2) USp(2N) theories. The description is performed explicitly for N = 1, 2, 3, 4, 5 and also for N = 3k + 2, which generalizes the situation in N = 2, 5. For N = 1 a triangle identity is obtained. For N = 2, 5 it is found that the realization of duality implies slight variations of a star-triangle relation type (STR type). The values N = 3, 4 are associated to a similar version of the asymmetric STR. The second case is a new duality for 2d$$ \mathcal{N} $$ N = (0, 2) USp(2N) theories with matter in the antisymmetric tensor representation that arises from dimensional reduction of 4d$$ \mathcal{N} $$ N = 1 USp(2N) Csáki-Skiba-Schmaltz duality. It is shown that this duality is associated to a triangle type identity for any value of N. In all cases Boltzmann weights as well as interaction and normalization factors are completely determined. Finally, our relations are compared with those previously reported in the literature.

Normalization Factors of Jordan's Diplomatic Relationship with Qatar: Dynamics Analysis of Area Security and National Identity

Jordan as a country that is flanked by several countries in conflict and does not have a wealth of natural resources, making alliances very important for the survival of the country. In meeting these needs, Jordan often allies with hegemonic actors for protective status as well as financial assistance. So in the scope of the Middle East, Jordan chose an alliance with the country of Saudi Arabia and its coalition in the Gulf region in the Gulf Cooperation Council. This alliance was continued until the 2017 Qatar diplomatic crisis, Jordan was forced to cut ties with Qatar so that the alliance with the Saudi kingdom would not be damaged. However, in 2019, Jordan normalized diplomatic relations with Qatar after their two-year stretch. This decision also came even though Saudi Arabia and its coalition had not normalized with Qatar itself, and could potentially jeopardize Jordan's existence if it did so because it was against the Saudi coalition. So the researcher tries to analyze this phenomenon using regional security theory and the level of national identity analysis. Researchers later found that the motive behind the decision was Qatar's support for the protective status of Jordan's Jerusalem.

Model-Based Feature Selection and Clustering of Rna-Seq Data for Unsupervised Subtype Discovery

Clustering is a form of unsupervised learning that aims to un-cover latent groups within data based on similarity across a set of features. A common application of this in biomedical research is in delineating novel cancer subtypes from patient gene expression data, given a set of informative genes. However, it is typically unknown a priori what genes may be informative in discriminating between clusters, and what the optimal number of clusters are. Few methods exist for performing unsupervised clustering of RNA-seq samples, and none currently adjust for between-sample global normalization factors, select cluster-discriminatory genes, or account for potential confounding variables during clustering. To address these issues, we propose the Feature Selection and Clustering of RNA-seq (FSCseq): a model-based clustering algorithm that utilizes a finite mixture of regression (FMR) model and utilized the quadratic penalty method with a SCAD penalty. The maximization is done by a penalized Classification EM algorithm, allowing us to include normalization factors and confounders in our modeling framework. Given the fitted model, our framework allows for subtype prediction in new patients via posterior probabilities of cluster membership. Based on simulations and real data analysis, we show the advantages of our method relative to competing approaches.

IMPROVED EMPIRICAL LIKELIHOOD FUNCTION BASED ON NORMALIZATION-DEPENDENT REPLICATE MEASUREMENTS

Based on $n$ replicate measurements that require known normalization factors and assuming an underlying normal distribution for individual measurements but with unknown standard deviation, a combined likelihood function is derived that takes the form of a Student’s $t$-distribution with $\nu = n-1$ degrees of freedom and $t=(\psi -\overline{Y})/s$, where $\psi $ is the true value of the measurement quantity calculated from the forward model, and $\overline{Y}$ and $s$ are average and standard error of the mean obtained from the $n$ measurements defined with weighting proportional to the inverse of the normalization factor squared. Assuming an underlying triangle distribution rather than a normal distribution does not produce a large change for six replicates. Examples of replicate data from an animal study and sequential occupational urine and fecal monitoring are given. The use of the empirical likelihood function in data modeling is discussed.